Adaptive search results for multimedia search queries

ABSTRACT

Certain embodiments involve adaptive search results for multimedia search queries to provide dynamic previews. For instance, a computing system receives a search query that includes a keyword. The computing system identifies, based on the search query, a video file having keyframes with content tags that match the search query. The computing system determines matching scores for respective keyframes of the identified video file. The computing system generates a dynamic preview from at least two keyframes having the highest matching scores.

TECHNICAL FIELD

This disclosure generally relates to image processing. Morespecifically, but not by way of limitation, this disclosure relates togenerating adaptive search results for multimedia search queries.

BACKGROUND

Image processing systems are used for creating appealing images that aredisplayed with online services that provide digital forums in which endusers may interact with online content (e.g., by browsing multimediacontent, purchasing multimedia content, commenting on multimediacontent, sharing multimedia content, etc.). Image processing systems usemodeling algorithms that involve techniques such as content filtering,pattern recognition, semantic relationship identification, userprofiling, etc. These image processing algorithms enable users to searchfor and locate desirable multimedia content related to various contentcategories of interest.

SUMMARY

The present disclosure includes generating adaptive search results formultimedia search queries of keywords related to multimedia content. Inone example, a computing system receives a search query that includes akeyword. The computing system identifies, based on the search query, avideo file that matches the search query. The computing systemdetermines matching scores for respective keyframes of the identifiedvideo file. A matching score for a keyframe can be determined based on anumber of content tags associated with a respective keyframe that matchthe keyword. The computing system generates a dynamic preview from atleast two keyframes having the highest matching scores. The dynamicpreview includes an arrangement of the keyframes, such as an on-hoverpreview video that includes the keyframes or a collage of the keyframes.

These illustrative examples are mentioned not to limit or define thedisclosure, but to aid understanding thereof. Additional embodiments andexamples are discussed in the detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

Features, embodiments, and advantages of the present disclosure arebetter understood when the following Detailed Description is read withreference to the accompanying drawings.

FIG. 1 depicts an example of a computing environment for generatingadaptive search results for multimedia search queries, according tocertain embodiments of the present disclosure.

FIG. 2 depicts a process for generating adaptive search results formultimedia search queries, according to certain embodiments of thepresent disclosure.

FIG. 3 depicts an example of a process for selecting at least twokeyframes within a video file using content tags, according to certainembodiments of the present disclosure.

FIG. 4 depicts an example of a keyframe within a video file, selectedusing content tags, according to certain embodiments of the presentdisclosure.

FIG. 5 depicts an example of generating, for display, a dynamic previewof at least two keyframes, according to certain embodiments of thepresent disclosure.

FIG. 6 depicts an example of a prior art search result.

FIG. 7 depicts another example of generating, for display, a dynamicpreview of at least two keyframes, according to certain embodiments ofthe present disclosure.

FIG. 8 depicts another example of generating, for display, a dynamicpreview generated from at least two keyframes, according to certainembodiments of the present disclosure.

FIG. 9 depicts an example of a computing system that can perform certainoperations described herein, according to certain embodiments of thepresent disclosure.

DETAILED DESCRIPTION

The present disclosure includes generating adaptive search results formultimedia search queries of keywords related to multimedia content. Forinstance, embodiments described herein can generate dynamic previews,such as collages or preview videos, using video frames that more closelymatch a search query than other video frames. For instance, if a videoincluded in a set of search results for the query “monkey” has a framewith a “monkey” tag and a frame with a “building” tag, the dynamicpreview can include the frame with the “monkey” tag. In this manner, thedynamic preview can more quickly indicate to a user that the video isrelevant to the search query.

The following non-limiting example is provided to introduce certainembodiments. In this example, an image processing system receives asearch query for a set of videos, such as a search query that containsthe keyword “monkey.” The image processing system can identify a set ofsearch results, such as videos with content tags or other data with theterm “monkey.” Examples of these content tags include a title tag (e.g.,a video titled “Monkey on Typewriter”), a summary tag (e.g., a videowith the summary “Monkey rides a bike to the store”), etc. The imageprocessing system can generate dynamic previews of these search results,where the dynamic preview includes a set of keyframes from a videosearch result that depicts content that is relevant to the searchresult.

For instance, a video file included in the search results could includekeyframes that are related to the search query. The image processingsystem can compute, for each keyframe, a respective matching score. Amatching score for a keyframe indicates a number of matches betweencontent tags for the keyframe and a keyword set in the search query.Examples of search terms that could be included in a keyword set includeone or more of a keyword specified by a user, a synonym of auser-specified keyword, a root word of a user-specified keyword (e.g.,the root word “ride” for the user-specified word “riding”), an umbrellaterm encompassing a user-specified keyword (e.g., umbrella terms such as“primate” or “mammal” for the user-specified term “monkey”), a categoryassociated with a user-specified keyword (e.g., categories including“funny monkey videos,” “cute baby monkeys,” or “monkey dancing videoclips” for the user-specified term “monkey”), a semantically-relatedword or phrase for a user-specified keyword (e.g., semantically words orphrases such as “primate,” “ape,” “chimpanzee,” “chimp,” “species ofgreat apes,” “new world monkeys,” or “old world monkeys” for theuser-specified term “monkey”), etc.

Returning to the simplified example above, a video with the summary“Monkey rides a bike to the store” could include frames depicting thestore (but not a monkey) and frames depicting a monkey riding a bike. Afirst set of frames depicting the monkey can include a content tag suchas “monkey,” and a second set of frames depicting the store can includea content tag such as “store.” A keyframe with the “monkey” content tagin the first set could have a higher matching score than a keyframe inwith the “store” tag in the second set. The image processing system canselect the keyframes having higher matching scores (e.g., the top nmatching scores, matching scores above a threshold score, etc.) toinclude in a dynamic preview.

Continuing with this example, the image processing system can generate adynamic preview for the video. One example of a dynamic preview is anarrangement of the selected keyframes as a collage. Another example of adynamic preview is an arrangement of selected keyframes that ispresented sequentially as a preview video clip, which can beautomatically played back. Because selected keyframes have highermatching scores, with respect to terms in the search query, theresulting dynamic preview includes visual content that quickly conveysrelevance to a user viewing the search result. For instance, in theexample above, a dynamic preview generated for a search query “monkey”includes sequential keyframes depicting a monkey riding a bicycle,rather than keyframes depicting a store without the monkey.

Certain embodiments provide improvements to computing systems used forsearching image content by automatically applying various rules of aparticular type. For instance, existing techniques for searching formultimedia-related products or services may present disadvantages.Current multimedia and image querying techniques may be able to presentimages of multimedia items related to search terms. But even if a videoincludes metadata matching a search term (e.g., the summary “monkey goesto the store” matching the search term “monkey”), a representative imagefor the video (e.g., a movie poster) may not depict the content withinthe video (e.g., the monkey) that is relevant to the search term.Presenting such a search result to a user may cause the user tomistakenly disregard the search result as irrelevant. Thus, existingsearch techniques involving video content can inconvenience a user byspending additional time to determine whether a search result isrelevant to the user's search query, or in some cases, incorrectlydiscarding an otherwise relevant search result.

These problems can be addressed by features described herein. Forinstance, dynamic previews generated using an image processing systemcan include image content that is specific to a search query. This imagecontent includes keyframes that are selected based on the keyframesthemselves (rather than just a video as a whole) having content tagsthat match a search query. These features can allow users to determinewhether a resulting video search result is relevant to their searchquery. Thus, embodiments described herein improve computer-implementedprocesses for querying image content, thereby providing a more suitablesolution for automating tasks previously performed by humans.

Example of a Computing Environment for Generating Adaptive SearchResults for Multimedia Search Queries

Referring now to the drawings, FIG. 1 depicts an example of a computingenvironment 100 for generating adaptive search results for multimediasearch queries, according to certain embodiments of the presentdisclosure. In the example depicted in FIG. 1, various client devices106 access an image processing system 114 via a data network 102. Theimage processing system 114 can include a keyframe detector 116, taggenerator 118, aesthetics engine 120, and an adaptive search engine 108.The adaptive search engine 108 can include a keyword search engine 110and a keyframe selector 112. In some embodiments, the image processingsystem 114 can be a single computing system that executes a keyframedetector 116, a tag generator 118, an aesthetics engine 120, a keywordsearch engine 110 and a keyframe selector 112. For instance, a clientdevice 106 that logs into a single website or other online service couldobtain access to the functions of these various engines, where one ormore server systems (e.g., in a cloud computing environment) executedifferent engines. In additional or alternative embodiments, a keyframedetector 116, a tag generator 118, an aesthetics engine 120, a keywordsearch engine 110 and a keyframe selector 112 could be implemented inseparate, independently operated computing systems.

In some embodiments, the image processing system 114 is used to detectkeyframes, generate content tags for keyframes, and use the content tagsto service search queries for video content. For instance, the imageprocessing system 114 is used to obtain, detect, and identify featuresassociated with one or more frames in a video file, where each frame isan image from the sequence of images in the video file. The imageprocessing system 114 can include one or more processing devices forexecuting suitable program code for performing one or more functions.Examples of this program code include the software engines depicted inFIG. 1, such as keyframe detector 116, tag generator 118, and aestheticsengine 120. Image processing system 114 can use one or more of theseengines to determine content tags for video content, select keyframes ofthe video content having content tags that match at least one keywordfrom a search query, and provide the selected keyframes to a renderingengine 122. The rendering engine 122 can generate a dynamic preview fromthe selected keyframes.

Some embodiments of the computing environment 100 include client devices106. For instance, the client devices 106 may be operated by cliententities (e.g., service providers, application developers, or othercontent providers) requesting augmentation or management of existingimages and video files to be augmented with techniques discussed herein.Such requests can be performed by sending video files directly to theadaptive search engine 108 or by requesting retrieval of video filesfrom multimedia database 104. In another example, the client devices 106may be operated by end users that desire to view an image of an emptycontainer filled with virtual elements. For instance, end usersoperating client devices may request retrieval of one or more realimages or one or more video files from a multimedia database 104, anadaptive search engine 108, a rendering engine 122, or any combinationof these.

Examples of a client device 106 include, but are not limited to, apersonal computer, a tablet, a desktop, a server, a mobile device, asmartphone, a processing unit, any combination of these devices, or anyother suitable device having one or more processors. A user of a clientdevice 106 uses various products, applications, or services supported bythe adaptive search engine 108 via the data network 102.

Each of the client devices 106 are communicatively coupled to theadaptive search engine 108 via the data network 102. Examples of thedata network 102 include, but are not limited to, internet, local areanetwork (“LAN”), wireless area network, wired area network, wide areanetwork, and the like.

Examples of Operations for Keyword Searching

The adaptive search engine 108 can be used to search for video filesthat match a keyword set included in a search query from a client device106. For instance, as described in detail with respect to the variousexamples below, the adaptive search engine 108 can include code, such asa keyword search engine 110, that is executed to service a query for oneor more video files matching one or more search terms, i.e., a set ofkeywords included in a search query received from a client device. Theadaptive search engine 108 can service a query by finding video contentthat matches the query and providing, in response to the query, adynamic preview generated from the video content.

In an illustrative example, the keyword search engine 110 retrievesvideo files having content tags that match at least one keyword from akeyword set. The keyword set can include one or more keywords in asearch query received from a client device 106. In some embodiments, thekeyword set can also include variants of one or more keywords includedin a user's search query. Examples of a variant of a keyword include asynonym of the keyword, a synonymous word or phrase for a phrase havingtwo or more keywords, a root word of a keyword (e.g., “search” insteadof “searching”), etc.

In the example depicted in FIG. 1, the adaptive search engine 108executes the keyword search engine 110 to obtain relevant video contentrelated to a keyword set from a search query. For instance, the keywordsearch engine 110 determines that content tags or other metadata of avideo matches at least some of the keyword set from a search query. Thekeyword search engine 110 provides the keyframe selector 112 with accessto the video content (e.g., one or more video files).

The keyframe selector 112 identifies keyframes from the video contentthat has been identified by the keyword search engine 110 as matching akeyword set from a search query. To do so, the keyframe selector 112 cancompute matching scores for each of the identified keyframes. A matchingscore for a given keyframe indicates a number of matches between thekeyword set and content tags associated the keyframe. As a simplifiedexample, a search query could include the words “monkey” and “sun,” akeyword set could include the synonyms “primate” and “daytime,” and akeyframe depicting a jungle scene could be associated with a firstcontent tag identifying a feature in the keyframe as a “primate” and asecond content tag identifying a feature in the keyframe as “daytime.” Afeature could include a set of repetitive low-level image components(e.g., pixel values, edges, corners, blobs, ridges, or changes incurvature, intensity, or spatial location). Additionally oralternatively, a feature could include a set of higher-level imagecomponents (e.g., candidate objects, multi-scale features, spatialrelationships, semantic relationships, three-dimensional movement,etc.). In this example, the keyframe could have a matching scoreindicating two matches between content tags of the keyframe and thekeyword set (i.e., the match on “daytime” and the match on “primate”).

In some embodiments, the keyframe selector 112 determines a ranking ofthese received keyframes. The keyframe selector 112 can rank thekeyframes for a given video file by sorting the matching scoresaccording to a number of matches between the keyword set and the contenttags in the keyframes to create a keyframe set. In one example, thekeyframe selector 112 could determine, from the matching scores, that afirst keyframe of a video file has a first number of matches between thekeyword set and content tags associated with the first keyframe, andcould further determine that a second keyframe of a video file has asecond number of matches between the keyword set and content tagsassociated with the second keyframe. The keyframe selector 112 couldrank the first keyframe higher than the second keyframe if the firstnumber of matches is greater than the second number of matches.

In another example, the keyframe selector 112 could determine that afirst keyframe of a video file has a first normalized matching score,and could further determine that a second keyframe of a video file has asecond normalized matching score. A normalized matching score could becomputed by multiplying a set of numerical matching scores, for a givenkeyframe set, by a factor to create a common scale that is easily sortedaccording to their adjusted values. In one example, a range ofnormalized matching scores from 0 (for the lowest n number of matches)to 1 (for the highest n number of matches) could be used for scoring thenumber of matches for all of the keyframes in a keyframe set. Forinstance, a normalized score for a first keyframe could be computed asthe number of matches for the keyframe divided by the number of featuresdetected in the keyframe. In a simplified example, excluding detectedfeatures that are common to the keyframe set, a keyframe with threeadditional detected features that are common to two matches (i.e., twofeatures of a highest amount of three additional detected featuresmatching the keyword set, using the number of matches associated withthe keyframe having the highest number of matches in the keyframe set)would use a factor of one-third multiplied by the two additional matchesto compute a normalized matching score of 0.67, whereas a secondkeyframe with five detected features and two matches (i.e., two featuresof the highest amount, of the keyframe set, of five additional detectedfeatures matching the keyword set) would use a factor of one-fifthmultiplied by the two matches to compute a normalized matching score of0.40. The keyframe selector 112 could rank the first keyframe higherthan the second keyframe if the first normalized matching score isgreater than the second normalized matching score.

In some embodiments, the keyframe selector 112 ranks the keyframes for agiven video file using the number of matches between the keyword set andthe content tags in the keyframes, as described above, in combinationwith one or more additional parameters. For instance, a rank for a givenkeyframe might be a weighted sum of a matching score and one or moreadditional parameters, where one weight is applied to the matchingscore, another weight is applied to a first additional parameter, yetanother weight is applied to a second additional parameter, etc.

One example of these additional parameters is a set of aesthetic scorescomputed by the aesthetics engine 120. For instance, in the examplesabove, considering only match scores might cause a first keyframe to beranked higher than a second keyframe. But if the keyframe selector 112determines that the second keyframe has an aesthetic score greater thanan aesthetic score of the first keyframe, and the difference between thematching scores of the first and second keyframes is sufficiently small(e.g., below a threshold amount), then the keyframe selector 112 couldrank the second keyframe higher than the first keyframe. Another exampleof an additional parameter is a diversity or uniformity for the selectedkeyframes. For instance, in the examples above, considering only matchscores might cause a first keyframe to be ranked higher than a secondkeyframe. But if the keyframe selector 112 determines that including thesecond keyframe will result in greater diversity of semantic content inthe dynamic preview and that difference in the matching scores betweenthe first and second keyframes is sufficiently small, the keyframeselector 112 could rank the second keyframe higher than the firstkeyframe.

The keyframe selector 112 stores a sorted list of the ranked keyframes.The rankings of the keyframes are used to generate a dynamic preview. Insome embodiments, the dynamic preview can be a collage. In additional oralternative embodiments, the dynamic preview can be an on-hover previewvideo that includes a selected set of keyframes. The rendering engine122 can generate a dynamic preview of adaptive search results depictingat least two keyframes in a particular arrangement. For instance, therendering engine 122 receives, from an adaptive search engine 108, a setof selected keyframes or information can be used by the rendering engine122 to retrieve the keyframes (e.g., a location of the video file andtimestamps of the selected keyframes).

The rendering engine 122 can combine the selected keyframes into adynamic preview. Examples of combining the keyframes include blending,layering, overlaying, merging, splicing, or any other suitable visualintegration technique. The rendering engine 122 outputs the dynamicpreview of adaptive search results to one or more client devices 106. Inone example, the rendering engine 122 may blend or layer keyframes in adynamic preview during a transition in video clip, e.g., combiningkeyframes by simultaneously increasing a transparency of a displayedfirst keyframe while decreasing the transparency a fully transparentsecond keyframe, to provide a smoother visual transition betweennon-sequential selected keyframes. In another example, the renderingengine 122 can splice keyframes from temporally distinct portions tocreate a dynamic preview that is a video clip for playback. In someexamples, the rendering engine 122 can transcode and merge keyframeswith different codecs into a dynamic preview. In another example, thedynamic preview can create a collage by partially or completelyoverlaying one keyframe with another with any suitable amount oftransparency, translucence, or opacity.

For illustrative purposes, FIG. 1 depicts an image processing system114, a keyframe detector 116, a tag generator 118, an aesthetics engine120, and an adaptive search engine 108 having a keyword search engine110 and a keyframe selector 112. But the image processing system 114 andadaptive search engine 108 can include any number of systems,sub-systems, or program code(s) to generate adaptive search results forone or more keywords, which may include one or more search queries.Further, any number or type of adaptive search results may be generatedfor client devices 106 with the adaptive search engine 108.

Examples of Video-Processing Operations to Facilitate Keyword Searching

The image processing system 114 can perform one or more video-processingoperations to facilitate keyword-based search queries for videos.Examples of these operations include detecting keyframes within a video,generating content tags for a video, and assessing aesthetic qualitiesof different keyframes within a video.

In an illustrative example, the image processing system 114 receives avideo file with one or more scenes. The image processing system 114 canexecute the keyframe detector 116 to divide the video file into segmentsby determining one or more keyframes associated with major scenes withinthe video file. An example of a major scene is a video segment thatdepict substantially similar features identified within a sequence ofimages contained within the video file.

The keyframe detector 116 identifies features from video frames using asuitable feature-detection algorithm. Examples of a suitablefeature-detection algorithm include a binary robust independentelementary features (“BRIEF”) algorithm, an oriented FAST and rotatedBRIEF (“ORB”) algorithm, and a features from accelerated segment test(“FAST”) algorithm. The keyframe detector 116 creates video segments(e.g., temporal segments, topical segments, clustered segments,multi-scale segments, motion segments, chunks, etc.) from the features,where each video segment includes frames with a common set of featuresand thereby depicts a major scene from the video file. Using these videosegments, the keyframe detector 116 can determine a number of featuresin each frame that match features shared by other frames within thegiven video segment.

The keyframe detector 116 can compute a frame score for each framewithin the video segment. The frame score indicates a number of matchesbetween features of a given frame and features of other frames within avideo segment (e.g., a scene depicted by a set of frames). Each framescore is computed from the number of matching features between the frameand other members of the video segment in combination with otherqualities of frames (e.g., aesthetic qualities). The keyframe detector116 sorts the frames in order of their respective frame scores. Thekeyframe detector 116 can select one or more keyframes associated with agiven major scene, a set of frames with frame scores that meet athreshold criteria. For instance, the keyframe detector 116 could selecta frame with the highest frame score, a frame having a frame score thatexceeds a threshold frame score specified by a user preference, etc.

Another example of a pre-processing operation is generating contenttags. For instance, in some embodiments, a video file can have contenttags associated with one or more frames prior to the image processingsystem 114 servicing a search query. For instance, a video file storedin the multimedia database 104 could include content tags that have beenapplied using computing systems other than the image processing system114. However, in additional or alternative embodiments, prior to theimage processing system 114 servicing a search query, a video file canhave insufficient content tags for matching the video file to a searchquery, even if the video file includes content that would be relevant tothe search query. For instance, such a video file can have insufficientcontent tags if the video file lacks any content tags at all, or thevideo file can have insufficient content tags if the video file onlyincludes content tags that do not match any term in the search query.

The image processing system 114 can determine such an insufficiency of avideo file's content tags. If the image processing system 114 determinesthat a video file's content tags are insufficient, the image processingsystem 114 can perform one or more tagging operations. For instance, thekeyframe detector 116 can select certain keyframes representing majorscenes within the video file and send the selected keyframes to the taggenerator 118. In additional or alternative embodiments, the imageprocessing system 114 can determine an insufficiency or absence ofcontent tags in a video file, or a set of video files, before using thekeyframe detector 116 to select a keyframe set associated with majorscenes. Such a determination would allow the keyframe detector 116 toidentify a keyframe set more accurately, with the increased number ofcontent tags available for processing.

The tag generator 118 can generate content tags associated with selectedkeyframes as a keyframe set. For instance, the tag generator 118 candetect one or more objects present in each of the keyframes. The taggenerator 118 generates a content tag for a frame that describes orotherwise indicates on one or more objects detected in the keyframe. Forinstance, such a content tag could indicate a mapped location of adetected object (or set of objects) depicted in a keyframe. The taggenerator 118 transmits, to the adaptive search engine 108, data thatidentifies or includes the keyframe set and the generated content tags.

The tag generator 118 can perform the object detection described aboveusing one or more suitable deep learning techniques. Examples ofsuitable deep learning techniques include techniques using a deep neuralnetwork, a convolutional neural network, etc. In one example, the taggenerator 118 uses a deep residual network (e.g., ResNet-101) trainedfor deeper image recognition in combination with a search technique thatselectively searches particular regions within a frame (e.g., a regionconvolutional neural network (“R-CNN”), Fast R-CNN, or Faster R-CNN).

In some examples, a Faster R-CNN model can be pre-trained using apredetermined set or subset of images, objects, and classes orcategories of images or objects. For instance, the Faster R-CNN modelcan receive an initial transfer of images imported from an existingdatabase that includes verified content tags. Using these content tags,associated with spatially-located features within the images, the FasterR-CNN model can more quickly and accurately assign content tags tofeatures in images. The combination of the deep residual network and thesearch technique can expedite detection of objects within keyframes. Insome embodiments, the overall processing speed of the image processingsystem 114 can be increased by limiting objects that may be identifiedby the tag generator 118 to any number of classes or categories ofobjects, e.g., an open images dataset.

In some embodiments, the tag generator 118 data that identifies orincludes the keyframe set and their respective content tags to theaesthetics engine 120. The aesthetics engine 120 can compute, for eachkeyframe, a respective aesthetic score from one or more aestheticparameters. Examples of these aesthetic parameters may include a globalquality score, a balancing element, a color harmony, a level of visualinterest, a depth of field, a quality of lighting, an amount of motionblur, an object emphasis, an amount of repetition, a rule of thirds(e.g., an image depicting visual aesthetics divided into thirds), anamount of symmetry, or a vivid color. The aesthetics engine 120 computesan aesthetic score for a given keyframe that indicates an amount of eachaesthetic parameter.

For instance, the aesthetics engine 120 can use one or more thresholdvalues to evaluate an amount of vivid colors. Specifically, usingchrominance data (e.g., an amount of brightness, hues, saturation ofhues, etc.) obtained from a sensor (e.g., a vectorscope), the aestheticengine 120 can determine whether the keyframe contains an amount ofchrominance to indicate the keyframe belongs to a particular level ofvivid colors. In another example, the aesthetics engine 120 can usepattern recognition and edge detection that indicates an amount ofmotion blur in a particular keyframe. Similarly to the thresholding ofvivid colors, the aesthetics engine 120 can determine a relative motionblur score that is a parameter included in the respective aestheticscore.

In some embodiments, the aesthetics engine 120 can compute an aestheticscore using numerical values that are assigned to various aestheticparameters. For instance, the aesthetics engine 120 can determinenumerical values based on threshold levels for a set of aestheticparameters. In one example, aesthetics engine 120 can select a set offive parameters (e.g., a global quality score, a quality of lighting, arule of thirds, an amount of symmetry, and an amount of vivid colors),each having an aesthetic score that is an assigned numerical valuebetween zero (e.g., a lowest amount possible such as a black, blank, ornull frame could be a zero for an amount of vivid colors) and ten (e.g.,a highest amount possible such as a rule of thirds having a frame havinga number of pixels that are divided and grouped into three distinct andequal parts). The aesthetics engine 120 can determine, for eachkeyframe, a respective total aesthetic score (out of a maximum aestheticscore of 50) by summing each of the aesthetic scores for a particularkeyframe.

In some examples, the aesthetics engine 120 can determine a set of totalaesthetic scores using all of the frames within a particular videosegment (e.g., a temporal or topical segment), a keyframe set (e.g.,representations of major scenes), a keyframe subset (e.g., keyframesassociated with major scenes having a minimum threshold aestheticscore), or any groupings of frames discussed herein. The aestheticsengine 120 can rank sets of total aesthetic scores by sorting these setsof total aesthetic scores by normalizing the values of the sets usingthe number of frames or keyframes within each set. In some examples, thekeyframe selector 112 can select one or more keyframes, a keyframe set,a video segment, or a keyframe subset using a ranked set of totalaesthetic scores provided by the aesthetics engine 120.

In some embodiments, the computation of the aesthetic score may useweights that are applied to various aesthetic parameters. The weightscan be obtained from an aesthetic model that uses weighting factorsassociated with aesthetic parameters that indicate the relativeimportance of each aesthetic parameter. Continuing with the examples ofthe aesthetic parameters discussed above, an aesthetic model mayprioritize the presence of vivid colors as a positive aestheticparameter with a higher priority than a negative aesthetic parameterwith of an amount of motion blur. In a simplified example, theaesthetics engine 120 may use a single threshold value for each of theamount of vivid colors and the amount of motion blur to determine athreshold level associated with a particular keyframe. For instance, akeyframe having a determined amount of vivid colors and amount of motionblur can be categorized by the aesthetics engine 120 as having an amountof the aesthetic parameter that is “high” or “low,” respectively, usingthe threshold value. In some examples, the aesthetics engine 120 cancategorize the amounts of any number of aesthetic parameters by applyingnumerical values that are associated with one or more threshold levels,for each respective aesthetic parameter.

In one example, a first keyframe may have a “high” amount of vividcolors and a “high” amount of motion blur, and a second keyframe mayhave a low amount of vivid colors and a low amount of motion blur. Ifthe aesthetics engine 120 were to treat both aesthetic parameters asequal factors, then the positive aesthetic parameter indicating theamount of vivid colors would negate the negative aesthetic parameterindicating the amount of motion blur for both the first keyframe and thesecond keyframe. But an aesthetics engine 120 using weighted factors foraesthetic parameters (e.g., assigning a higher weight to vivid colorsthan to motion blur) could determine that the first keyframe should beranked higher than the second keyframe by calculating a higher aestheticscore of the first keyframe, since the first keyframe has the aestheticparameter (vivid colors) that is assigned the higher weighting factor.

In some examples, the aesthetics engine 120 applies a numericalweighting factor, associated with numerically assigned aestheticparameters, to a set of aesthetic parameters in order to obtain a set ofadjusted aesthetic parameters. The aesthetics engine 120 can use theseadjusted aesthetic parameters to sort and rank a keyframe set accordingto an aesthetics model. The aesthetics engine 120 transmits, to theadaptive search engine 108, data that identifies or includes thekeyframe set, their respective content tags, and their respectiveaesthetic score to the adaptive search engine 108.

Examples of a Process for Generating Adaptive Search Results forMultimedia Search Queries

FIG. 2 depicts a process 200 for generating adaptive search results formultimedia search queries, according to certain embodiments of thepresent disclosure. In some embodiments, one or more processing devicesimplement operations depicted in FIG. 2 by executing suitable programcode (e.g., keyword selection engine 110, keyframe selector 112,rendering engine 122, etc.). For illustrative purposes, the process 200is described with reference to certain examples depicted in the figures.Other implementations, however, are possible.

At block 202, the process 200 involves receiving a search query thatincludes a keyword set. For example, the image processing system 114 canimplement the block 202. The image processing system 114 can receive,during a session with a client device 106, the search query from theclient device 106 via the data network 102. The image processing system114 can identify a keyword set from the search query. In someembodiments, the keyword set includes one or more keywords provided bythe client device 106. In additional or alternative embodiments, thekeyword set includes variants of one or more keywords provided by theclient device 106 (e.g., synonyms of a user-provided keyword, a rootword of a user-provided keyword, etc.).

In some embodiments, the image processing system 114 can receive thesearch query directly from one or more client devices 106. In additionalor alternative embodiments, the image processing system 114 can receivethe search query from a third-party computing system, such one or moreserver systems that host a search engine (e.g., adaptive search engine108) accessed by one or more client devices 106.

The image processing system 114 can service the search query andgenerate one or more dynamic previews for search results that match oneor more search terms in the search query. Blocks 204-208 can implement astep for generating a dynamic preview of video content matching thekeyword.

For example, at block 204, the process 200 involves identifying one ormore video files, from a video library, each video file having a set ofkeyframes associated with content tags that match at least one keywordfrom the keyword set. For example, the image processing system 114 canexecute the adaptive search engine 108 to implement block 204. Theadaptive search engine 108 can access a set of video files and contenttags associated with the video files. The adaptive search engine 108 cancompare the content tags to the keyword set. If a particular videoincludes a content tag having a keyword from the keyword set, theadaptive search engine 108 identifies the video as a search result forthe search query.

The adaptive search engine 108 can perform one or more suitableoperations for obtaining a set of video files that is compared, at block204, to the keyword set. In one example, the keyword search engine 110could retrieve a catalogue of multimedia files and select video filesfrom the catalogue. To do so, the keyword search engine 110 communicateswith multimedia database 104 to determine whether content tagsassociated with a particular video file includes a sufficient number ofcontent tags that match a keyword set in a search query received atblock 202. For instance, video file metadata could include content tagsidentifying a title, description, classification or category, timestamp,location information, duration, file size, resolution, etc. The keywordsearch engine 110 can use the video file metadata to determine whether anumber of matches between the keyword set and the video file's contentexceeds a threshold. If so, the keyword search engine 110 selects thevideo file as a search result for the search query received at block202.

In another example, the adaptive search engine 108 can compare thesearch query received at block 202 to one or more stored search queries.If the search query received at block 202 includes one or more searchterms that match one or more search terms from a stored search query,the adaptive search engine 108 can identify a set of video files thatwere provided as search results in a response to the stored searchquery. The adaptive search engine 108 can compare content tags for theidentified set of video files to the keyword set of the search queryreceived at block 202. If a particular video that matches the searchquery received at block 202 was also included in the search results forstored search query, the keyword search engine 110 can weight thatparticular video file search result more heavily (i.e., cause theparticular video file search result to be listed earlier in a list ofsearch results) relative to a video file that only appears in searchresults for the search query received at block 202.

At block 206, the process 200 involves selecting a keyframe subset froma video identified as a search result, where the keyframe subset is areduced set of keyframes previously selected using respective matchingscores that indicate a number of matches between the keyword set andcontent tags of the selected keyframes. For example, the imageprocessing system 114 can execute the keyframe selector 112 of theadaptive search engine 108 to implement block 206.

The keyframe selector 112 can compute matching scores for a keyframe setof a video that has been identified as a search result. The matchingscores indicate a closeness of a match between a given keyframe and thesearch query. For instance, if most of the features in a particularkeyframe are associated with content tags that include at least onekeyword from the keyword set, the keyframe may have a higher matchingscore. Conversely, if a minority of the features in a keyframe areassociated with content tags that include at least one keyword from thekeyword set, the keyframe may have a lower matching score.

The keyframe selector 112 can rank the keyframe set in accordance withrespective matching scores, as described above with respect to FIG. 1.In some embodiments, the keyframe selector 112 can rank the keyframe setin accordance with their matching scores in combination with additionalparameters, such as aesthetic scores. The keyframe selector 112 stores asorted list of the ranked keyframe set. As discussed above, in someembodiments, the keyframe selector 112 can determine a number ofkeyframes from the ranked keyframe set (e.g., a keyframe subset) to beincluded in a dynamic preview.

For instance, in embodiments in which the dynamic preview is a thumbnailcollage, the keyframe selector 112 can determine a total number ofkeyframes to be included in the thumbnail collage. In one example, thekeyframe selector 112 can determine a number of keyframes to include oneor more user preferences. User preferences can include a time duration,a dynamic preview resolution, a video playback speed, a number of imagesto be included in a collage, a number of search results to be displayedsimultaneously, an aspect ratio, an audio setting, a loop setting, arefresh rate, a caption preferences, an automatic playback, or anycombination of these.

In one example, the keyframe selector 112 can access a user profile,that stores one or more user preferences, for a user that is associatedwith a client device 106 from which a search query is received. The userprofile can include data indicating that search results are to bedisplayed as a thumbnail collage of n images. In this case, the keyframeselector 112 determines the highest ranking number of n keyframes toprovide to a rendering engine 122 for a thumbnail collage. The userprofile can also include a default value of n or a value of n that hasbeen specified via one or more user inputs.

In another example, the keyframe selector 112 can determine a maximumnumber of keyframes to include, where the maximum number may override auser preference. For instance, a maximum value of n can be determinedwith parameters that may include one or more of a display capability ofa client device 106 from which a search query is received, a displayscreen size of a client device 106 from which a search query isreceived, frame resolutions of videos matching a search query, etc. Ifthe keyframe selector 112 has determined a maximum number of keyframesto include in a dynamic preview, the keyframe selector 112 could selecta value of n that is the smaller of the determined maximum number ofkeyframes and the user-specified number of keyframes.

In these embodiments, the keyframe selector 112 can generate an on-hoverpreview in accordance with one or more user preferences. An on-hoverpreview can include playback of a video clip, a popup, a graphicaldisplay, an introductory video, a GIF, an advertisement, an animation,etc. In some examples, the keyframe selector 112 can generate anon-hover preview that includes one or more triggers. Examples oftriggers can include a cursor movement, mousing, a mouseover, a mouseclick, proximity sensing (e.g., a sensor for detecting a location thatis proximate, nearby, or within a predefined distance between a cursor,pointer, a user's finger, stylus, etc. and an image representing theon-hover preview), or an interaction with a touch surface, a gesture,etc. For instance, a video player can begin playback of a video clip ifa processor of a client device, in communication with a proximitysensor, receives a sensor signal indicating that an object (e.g., auser's finger) is approaching a graphical representation of an on-hoverpreview, then the processor can determine playback is being initiated bya trigger associated with the user interaction and begin playback of theon-hover preview.

In one example, the keyframe selector 112 can access a user profile fora user that is associated with a client device 106 from which a searchquery is received. The user profile can include data indicating a userpreference for search results to be displayed using an on-hover previewvideo. In some examples, such a user preference for an on-hover previewcan indicate that the on-hover preview video should have auser-specified duration parameter. Examples of user-specified durationparameter include a minimum time duration, a maximum time duration, atime duration falling within a specified a range of durations, etc.

In these embodiments, the keyframe selector 112 can transmit a commandto a rendering engine 122 for rendering the on-hover preview video. Thecommand can specify that each of the n highest-ranked keyframes are tobe displayed in a specified sequence (i.e., by ordering the keyframes inaccordance with their sequentially increasing timestamps). The commandcan also specify that each keyframe is to be assigned a particulardisplay duration such that a total time duration of the on-hover previewvideo complies with the user-specified duration parameter.

At block 208, the process 200 involves generating, for display, adynamic preview from a selected keyframe set. For example, the imageprocessing system 114 can execute the rendering engine 122 to generatethe dynamic preview. A dynamic preview includes an arrangement of theselected keyframe set. The selected keyframe set includes at least twokeyframes selected by keyframe selector 112 at block 206. In someembodiments, the rendering engine 122 can generate a dynamic preview inaccordance with a command from the keyframe selector 112 that identifiesparticular keyframes, a particular number of keyframes, a particularsequence of keyframes, etc. Examples of a dynamic preview, such as athumbnail collage or an on-hover preview, are described in furtherdetail below with respect to FIGS. 4, 5, 7, and 8.

At block 210, the process 200 involves outputting the dynamic preview toa display device. For example, the rendering engine 122 outputs thedynamic preview depicting the keyframe set to one or more client devices106 to a storage device accessible to the image processing system 114.The image processing system 114 can retrieve the dynamic preview fromthe storage device and transmit the dynamic preview to a client device106. The client device 106 can display the dynamic preview in agraphical interface that includes one or more search results for thesearch query. In some embodiments, the image processing system 114 cantransmit the dynamic preview depicting the keyframe set directly to oneor more client devices 106. In additional or alternative embodiments,the image processing system 114 can transmit the dynamic preview to athird-party computing system, such one or more server systems that hosta search engine (e.g., adaptive search engine 108) accessed by theclient device 106.

FIG. 3 depicts a process 300 for selecting one or more keyframes withina video file using content tags, according to certain embodiments of thepresent disclosure. The process 300 can be used to identify thekeyframes used in block 206 of the process 200. In some embodiments, oneor more processing devices implement operations depicted in FIG. 3 byexecuting suitable program code (e.g., keyframe detector 116, taggenerator 118, etc.). For illustrative purposes, the process 300 isdescribed with reference to certain examples depicted in the figures.Other implementations, however, are possible.

At block 302, the process 300 involves detecting features in frames of avideo file. For example, the image processing system 114 can execute thekeyframe detector 116 of the adaptive search engine 108 to implement theblock 302. The keyframe detector 116 can scan content of a videoidentified as a search result and detect features within the video file.

In some embodiments, the image processing system 114 can create areduced-resolution version of the video search result, therebyincreasing processing efficiency for the scanning process. For example,the image processing system 114 can create a copy of the video searchresult, resize frames within the copied video (e.g., by transcoding ahigh-definition resolution, such as 1920×1080, to a reduced resolution,such as 512×288), and provide the resized frames to the keyframedetector 116 for scanning. In the scanning process, the keyframedetector 116 can divide the video file into segments by determiningkeyframes associated with major scenes within the video file. Thekeyframe detector 116 uses a suitable feature detector (e.g., ORB, FAST,BRIEF, etc.) to identify features of the frames that are used todetermining which frames are keyframes of major scenes.

At block 304, the process 300 involves identifying major scenes in thevideo file with the detected features of the frames. For example, thekeyframe detector 116 of can implement the block 304. To do so, thekeyframe detector 116 creates video segments of frames for each majorscene within the video file. The keyframe detector 116 compares thescanned features (e.g., regions, edges, key points, pixel values,feature vectors, etc.) of a current frame with matching features of animmediate previous frame in the sequence. The keyframe detector 116groups the frames by sorting frames using a predetermined number ofmatching features between the frames. For instance, if a first videosegment includes frames that share a first set of detected features anda second video segment includes frames that share a second set ofdetected features, a particular frame is assigned to the first videosegment if that particular frame includes more features from the firstset of detected features than the second set of detected features.

As an example, the keyframe detector 116 matches features of a previousframe with features of a current frame. The keyframe detector 116determines whether a number of features matching the previous frame withrespect to a threshold number of features. If the keyframe detector 116determines that the number of matching features is less than thethreshold number of features, then the keyframe detector 116 temporarilymarks the current frame as a first frame of a new major scene. Thekeyframe detector 116 can then verify the beginning of the new majorscene by determining whether the temporarily marked frame includes anumber of matching features greater than the threshold number offeatures for a predetermined number of subsequent k frames. If thekeyframe detector 116 determines the number of matching features isgreater than the threshold number of features, then a current frame isincluded in a video segment of frames associated with the previousframe.

At block 306, the process 300 involves determining, for each majorscene, a frame score that indicates a number of features that match theother frames within the respective major scene. The keyframe detector116 determines a number of features, for each frame, that match featuresshared by other frames within the particular video segment, assigning aframe score for each frame within the video segment using the number ofmatching features.

The keyframe detector 116 can assign a frame score to each frame withina video segment utilizing a total number of matching features. Thekeyframe detector 116 can sort the frames within a video segment in ahierarchical order of their respective frame scores. In someembodiments, the keyframe detector 116 can sort the frames within avideo segment based on a mean frame score.

In one example, the mean frame score can be determined using an averagenumber of features common to frames within a particular video segment.For instance, a particular video segment contains a number of n frames,each frame having a predetermined threshold number of features in commonthat is required to be included in the video segment. The keyframedetector 116 can determine, using previously identified featuresassociated with each frame, a total number of features f that is sharedby the respective frame and the other frames. The keyframe detector 116sums all of the features within the video segment (e.g., Σf_(n)) anddivides this sum by the number of frames n to obtain the mean framescore of the frames included in the video segment. The keyframe detector116 may use the mean frame score as a minimum threshold value to includea particular frame in a subset of keyframes to send to the keyframeselector 112. The keyframe detector 116 can select, as one or morekeyframes for the major scene, a set of frames with frame scores thatmeet a threshold criteria (e.g., one or more frames having the highestframe scores, one or more frames having frame scores above a thresholdor mean frame score, etc.).

In some embodiments, the keyframe detector 116 communicates with the taggenerator 118 to obtain additional content tags that the tag generatorapplies to keyframes of a video search result. As noted above, theadaptive search engine 108 can retrieve a video file having content tagsmatching a keyword set of a search query. But even though the video fileincludes content tags matching the keyword set, the content tags may notbe associated with keyframes that depict content that is relevant to thesearch query. The adaptive search engine 108 can execute the keyframedetector 116 and the tag generator 118 to generate content tags that areassociated with such keyframes, which can thereby facilitate thecreation of a dynamic preview that includes the content that is relevantto the search query.

For instance, the tag generator 118 can receive, from the keyframedetector 116, data identifying keyframes for the video file. The taggenerator 118 determines content tags associated with each keyframe asdescribed above, with respect to FIG. 1. The tag generator 118 detectsobject boundaries and their associated content tags, labeling andmapping a relative location of each detected object within a keyframe.In one example, the tag generator 118 detects objects within eachkeyframe and generates content tags for the detected objects using deeplearning techniques discussed above with respect to FIG. 1 (e.g., usingFaster R-CNN with ResNet-101). In some examples, tag generator 118 sortsthe keyframes using a limited set of criterion such as a maximum numberof keywords, synonyms, or combination of keywords and synonymsassociated with previous search queries provided by a user.

In additional or alternative embodiments, image processing system 114can increase processing efficiencies by generating content tags for areduced number of frames. For instance, image processing system 114executing tag generator 118 after receiving a keyframe set reduces anamount of content tags that need to be generated to only include thoserepresenting major scene, as identified by the keyframe detector 116,instead of identifying content tags for every frame within a video file.In some examples, the image processing system 114 can execute taggenerator 118 after the keyframe selector 112 determines a preliminary,reduced subset of candidate keyframes from a keyframe set (e.g., usingthreshold aesthetic scores to make such a determination, as discussedherein).

In some embodiments, the tag generator 118 can also increase theaccuracy of content tags by using more than one deep learning techniqueto detect anomalous keyframes and select the anomalous keyframes. Ananomalous keyframe can include detectable features, associated with theframe's spatial or temporal occurrence relative to spatially ortemporally adjacent frames, at either the pixel level or frame level(e.g., new objects, shapes, edges, pixel values, a movement of anobject, an unpredictable behavior associated with an object, etc.) thatdeviates significantly from a predicted pixel value or frame value. Insome examples, a significant deviation from a predicted pixel value orframe value can be a deviation from an intra-frame or inter-frameprediction (e.g., using a codec such as JPEG, MPEG) that represents astatistical deviation greater than a predetermined standard deviationfor a set of pixel values or frame values. For instance, the taggenerator 118 may use a convolutional neural network to detect anomalouskeyframes, extracting respective features pertinent to a particularkeyframe, and identifying such features as being associated with acontent tag.

In one example, the tag generator 118 can encode the extracted featuresinto a representation of focal points in the particular keyframe. Thefocal points of a keyframe could include, for example, a region ofinterest, a particular object, person, face, group of proximatelylocated objects within a region of an image, a centroid of an object inan image, etc. The encoded representation of focal points could include,for example, low-level visual components or high-level components,discussed above, which may be encoded (e.g., by an encoding model usinga linear model, a non-linear model, a hierarchical model, a voxelrepresentation model, a haystack model, or any other suitablecomputational model) to compress an amount of data associated with atagged focal point.

In another example, the tag generator 118 can determine a cluster ofrelated images associated with the encoded representation. For instance,a cluster of related images, such as images depicting statisticallysimilar objects (e.g., an object with an insignificant amount ofvariance that is below a standard deviation for the identification ofthe object) occurring in one or more sets of sequential frames, could beassociated with an encoded representation of focal points, such as avideo segment depicting a monkey riding bicycle and another depicting adog riding a bicycle. In this example, because the monkey and dog aresemantically related (e.g., animals) and are riding statisticallysimilar objects (e.g., bicycles) their respective video segments can beclustered together. The tag generator 118 can label the clustered imageswith one or more classifications. For instance, if the cluster of imagesdepicting monkey and dog bicyclists are associated with the encodedrepresentation of focal points such as a monkey, a dog, wheels,handlebars, pedals, etc., then the tag generator 118 can apply a contenttag to the images indicating that the images can be classified as“animals cycling,” “bike-riding mammals,” or “pets on bicycles.” The taggenerator 118 can provide the content tags for these images to thekeyframe selector 112.

At block 308, the process 300 involves selecting a set of keyframes thatrepresent major scenes using frame scores for each major scene. Thekeyframe selector 112 can implement block 308. For example, the keyframeselector 112 can receive keyframes from the keyframe detector 116 or thetag generator 118. The keyframe selector 112 selects one or morekeyframes from the received keyframes. In this example, a keyframe isselected if a ranking of the keyframe is sufficiently high (e.g., is oneof the higher ranks, exceeds a threshold rank, etc.).

The ranking of the keyframe is determined from a match between a keywordset from a search query and a number of content tags for the keyframe.For instance, keyframe selector 112 determines, for each receivedkeyframe, a keyframe weight taking into account a number of matchesbetween the content tags associated with the particular keyframe and thequeried keyword set. In additional or alternative embodiments, thekeyframe selector 112 can use one or more content tags provided by thetag generator 118 to select one or more keyframes with a highestmatching score relative to a predetermined set of criterion. In someexamples, the keyframe selector 112 utilizes information provided by thekeyword search engine 110, keyframe detector 116, tag generator 118,aesthetics engine 120, or any combination of information provided bythese as criterion to determine a highest matching score. The keyframeselector 112 provides the matching scores to rendering engine 122.

Examples of Dynamic Previews Generated from Keyframes

The following examples are provided to illustrate potential applicationsof the operations described above. In particular, FIG. 4 depicts anexample 400 of a keyframe within a video file, selected using contenttags, according to certain embodiments of the present disclosure. Inthis example, a keyframe 400 represents an image within a video fileidentified by keyframe detector 116 according to certain aspects ofembodiments discussed herein.

In the example depicted in FIG. 4, similar to block 204 of process 200,the image processing system 114 executes program code described herein(e.g., adaptive search engine 108, keyword search engine 110, keyframedetector 116, tag generator 118, etc.) to identify metadata associatedwith an image within a video frame (e.g., a creator, creation location,creation time, brand name, image title, one or more captions, keywords,technical details, digital rights, or any combination of these). In thisexample, the keyframe 400 depicts various features including jungle 402,sunshine 404, elephant 406, and water 408. In some embodiments, the taggenerator 118 creates content tags 410 from a subset of theabove-mentioned keyframe features.

For instance, content tag 412 identifies the feature jungle 402 as anelement of the keyframe 400, which associated with classification term“Jungle.” In some embodiments, content tag 412 can be classified withone or more additional classifications. For example, content tag 412 mayinclude additional classifications such as a background image type,genre classifications of “wildlife,” “forestation,” or “vegetation,” ora thematic classification “environmental scenery.” Similarly, theremaining content tags 410 can also be associated with multiple contenttags, classifications, or other contextual information discussed herein.

Continuing with the example keyframe 400, content tag 414 identifies thefeature sunshine 404 that is likewise associated with classificationterm “Sunshine.” Content tag 414 provide contextual information of thekeyframe 400, such as identifying one or more features as “background,”“lighting,” “brightness,” “nature,” “sun,” “star,” etc. In addition,content tag 416, identifying elephant 406 as “Elephant,” can also beclassified with an image type “foreground imagery,” a biologicalclassification “animal kingdom,” one or more geolocation classificationsof “African savannah,” “Southeast Asia,” or “zoo,” or a movie genre“children's movie character.” Content tag 418 identifies feature water408 as “Water,” but can be classified in broad or narrowclassifications. For instance, classifications such as “background” or“body of water” may be too broad to be helpful in selecting a keyframe,but image processing system 114 may have predetermined classificationsthat are narrower such as “standing water,” “knee-deep,” “shallowwaters,” etc. In some embodiments, multiple synonyms, classifications,or themes may be included in a content tag. In other examples, multiplecontent tags may be created and associated with each object identifiedby tag generator 118.

FIG. 5 depicts an example 500 of generating, for display, a dynamicpreview of at least two keyframes. Thus, the functions described in thisexample can be applied to implement the block 208 in the process of FIG.2. For instance, in the example 500 a user selects an option to performa multimedia search 502 that is sorted by relevance. The user inputs asearch query of “monkey,” which generates, for display, the dynamicpreview depicted in search result 504. In this example, three thumbnailimages are selected for the collage depicted in the search result 504,and the rightmost third image, depicting primates interacting,represents an extracted keyframe that was not one of the initiallydisplay images present in the user selection from multimedia search 502.

FIG. 6 depicts an example 600 of a prior art search result. In thisexample, a user inputs a search query 602 for the keyword “monkey.”However, unlike the dynamic previews for adaptive search resultsdescribed herein, the search results include an image 604 of a rightmostthumbnail image that depicts a video title set against a background of adesert landscape is apparently unrelated to the keyword “monkey.” Inthis example, the user would have little to no contextual information todecide whether the video corresponding to the image 604 is at allrelated to the keyword “monkey.”

FIG. 7 depicts another example 700 of generating, for display, a dynamicpreview of at least two keyframes, according to certain embodiments ofthe present disclosure. In this example, a user inputs a search query702 of two keywords: “lion” and “shark,” which can initiate a process200 of FIG. 2, causing the image processing system 114 to identify avideo file 704 according to the techniques described herein. In thisexample, the keyframe detector 116 identifies major scenes within thevideo file 704, extracting keyframes that are associated with relevantcontent tags. The keyframe selector 112 selects keyframe set 706associated with keywords “monkey,” “shark,” and “lion,” using theirrespective matching scores between the keyword set input by the user,e.g., “lion” and “shark,” in combination with a recent, previous searchquery of “monkey.”

In this example, keyframe selector 112 sends the selected keyframe set706 to rendering engine 122, which generates for display the dynamicpreview depicted in search result 708. In some embodiments, the threethumbnail images depicted in search result 708 are selected for thecollage in an order that reflects a user history of search queries(e.g., “monkey” is displayed in an uppermost location since itscorresponding search query preceded the query for “lion” and “shark”).And in some embodiments, the exemplary three thumbnail images depictedin search result 708 may be a default system setting that enables thesystem to limit inclusion of thumbnail images to a maximum number ofthumbnail images for a given collage image.

FIG. 8 depicts another example 800 of generating, for display, a dynamicpreview of at least two keyframes, according to certain embodiments ofthe present disclosure. In this example, a user search query is input todetermine a dynamic preview for adaptive search results according tocertain aspects of embodiments discussed herein. In the example 800depicted in FIG. 8, similar to block 202 of process 200, the imageprocessing system 114 receives a search query 802 from the user for thekeywords “Person A.” As discussed above, with respect to blocks 204 and206 of process 200, as well as the process 300, the adaptive searchengine 108 identifies a video file using the keyword set, identifieskeyframes within the video file with a threshold number of matchingcontent tags, and determines a matching score for each keyframe bysumming a number of matching content tags and the keywords. In example800, the rendering engine 122 generates, for display, a dynamic previewsimilar to block 208 of process 200.

In this example, the dynamic preview produced by rendering engine 122 isan on-hover preview video with three keyframes. Specifically, the searchresult 804 depicts an arrangement of the dynamic preview that initiallydisplays a first image 808. The first image 808 corresponds to keyframe1 of the on-hover preview video generated by the rendering engine 122. Auser can move cursor 806 to hover over (e.g., mouseover) the first image808 depicted in search result 804. The movement of the cursor 806 to acorresponding on-screen location associated with the first image 808initiates playback of the dynamic preview. In this example, playback ofthe on-hover preview video results in the subsequent display of a secondimage 810, corresponding to keyframe 2, and third image 812,corresponding to keyframe 3. In some embodiments, the playback of anon-hover preview can include playback of a looped video clip (e.g., aplayback loop), a short video clip, an introductory video, a GIF, anadvertisement, a series of graphics or still images (e.g., JPEG), anMPEG, an animation, a PNG, an MNG or any other suitable video format.

Example of a Computing System for Generating Adaptive Search Results forMultimedia Search Queries

Any suitable computing system or group of computing systems can be usedfor performing the operations described herein. For example, FIG. 9depicts examples of computing system 900 that executes an imageprocessing application 914. In some embodiments, the computing system900 also executes the adaptive search engine 108, as depicted in FIG. 9.In other embodiments, a separate computing system having devices similarto those depicted in FIG. 9 (e.g., a processor, a memory, etc.) executesthe adaptive search engine 108.

The depicted examples of a computing system 900 includes a processor 902communicatively coupled to one or more memory devices 904. The processor902 executes computer-executable program code stored in a memory device904, accesses information stored in the memory device 904, or both.Examples of the processor 902 include a microprocessor, anapplication-specific integrated circuit (“ASIC”), a field-programmablegate array (“FPGA”), or any other suitable processing device. Theprocessor 902 can include any number of processing devices, including asingle processing device.

The memory device 904 includes any suitable non-transitorycomputer-readable medium for storing data, program code, or both. Acomputer-readable medium can include any electronic, optical, magnetic,or other storage device capable of providing a processor withcomputer-readable instructions or other program code. Non-limitingexamples of a computer-readable medium include a magnetic disk, a memorychip, a ROM, a RAM, an ASIC, optical storage, magnetic tape or othermagnetic storage, or any other medium from which a processing device canread instructions. The instructions may include processor-specificinstructions generated by a compiler or an interpreter from code writtenin any suitable computer-programming language, including, for example,OpenCV, C, C++, C#, Visual Basic, Java, Python, Perl, JavaScript, andActionScript.

The computing system 900 may also include a number of external orinternal devices, such as an input device 912, a presentation device916, or other input or output devices. For example, the computing system900 is shown with one or more input/output (“I/O”) interfaces 908. AnI/O interface 908 can receive input from input devices or provide outputto output devices. One or more buses 906 are also included in thecomputing system 900. The bus 906 communicatively couples one or morecomponents of a respective one of the computing system 900.

The computing system 900 executes program code that configures theprocessor 902 to perform one or more of the operations described herein.The program code includes, for example, the image processing application914, the adaptive search engine 108, or other suitable applications thatperform one or more operations described herein. The program code may beresident in the memory device 904 or any suitable computer-readablemedium and may be executed by the processor 902 or any other suitableprocessor. In some embodiments, all modules in the image processingapplication 914 (e.g., the keyframe detector 116, tag generator 118,aesthetics engine 120, keyword search engine 110, keyframe selector 112,etc.) are stored in the memory device 904, as depicted in FIG. 9. Inadditional or alternative embodiments, one or more of the imageprocessing application 914 and the adaptive search engine 108 are storedin different memory devices of different computing systems. Inadditional or alternative embodiments, the program code described aboveis stored in one or more other memory devices accessible via a datanetwork.

The computing system 900 can access one or more of the image processingapplication 914 and the adaptive search engine 108 in any suitablemanner. In some embodiments, some or all of one or more of these datasets, models, and functions are stored in the memory device 904, as inthe example depicted in FIG. 9. For example, a computing system 900 thatexecutes the image processing application 914 can provide access to thekeyword search engine 110 by external systems that execute the adaptivesearch engine 108.

In additional or alternative embodiments, one or more of these datasets, models, and functions are stored in the same memory device (e.g.,one of the memory device 904). For example, a common computing system,such as multimedia database 104 depicted in FIG. 1, can host theadaptive search engine 108. In additional or alternative embodiments,one or more of the programs, data sets, models, and functions describedherein are stored in one or more other memory devices accessible via adata network.

The computing system 900 also includes a network interface device 910.The network interface device 910 includes any device or group of devicessuitable for establishing a wired or wireless data connection to one ormore data networks. Non-limiting examples of the network interfacedevice 910 include an Ethernet network adapter, a modem, and the like.The computing system 900 is able to communicate with one or more othercomputing devices (e.g., a computing device executing a keyframeselector 112) via a data network with the network interface device 910.

General Considerations

Numerous specific details are set forth herein to provide a thoroughunderstanding of the claimed subject matter. However, those skilled inthe art will understand that the claimed subject matter may be practicedwithout these specific details. In other instances, methods,apparatuses, or systems that would be known by one of ordinary skillhave not been described in detail so as not to obscure claimed subjectmatter.

Unless specifically stated otherwise, it is appreciated that throughoutthis specification discussions utilizing terms such as “processing,”“computing,” “calculating,” “determining,” and “identifying” or the likerefer to actions or processes of a computing device, such as one or morecomputers or a similar electronic computing device or devices, thatmanipulate or transform data represented as physical electronic ormagnetic quantities within memories, registers, or other informationstorage devices, transmission devices, or display devices of thecomputing platform.

The system or systems discussed herein are not limited to any particularhardware architecture or configuration. A computing device can includeany suitable arrangement of components that provide a result conditionedon one or more inputs. Suitable computing devices include multi-purposemicroprocessor-based computer systems accessing stored software thatprograms or configures the computing system from a general purposecomputing apparatus to a specialized computing apparatus implementingone or more embodiments of the present subject matter. Any suitableprogramming, scripting, or other type of language or combinations oflanguages may be used to implement the teachings contained herein insoftware to be used in programming or configuring a computing device.

Embodiments of the methods disclosed herein may be performed in theoperation of such computing devices. The order of the blocks presentedin the examples above can be varied—for example, blocks can bere-ordered, combined, and/or broken into sub-blocks. Certain blocks orprocesses can be performed in parallel.

The use of “adapted to” or “configured to” herein is meant as open andinclusive language that does not foreclose devices adapted to orconfigured to perform additional tasks or steps. Additionally, the useof “based on” is meant to be open and inclusive, in that a process,step, calculation, or other action “based on” one or more recitedconditions or values may, in practice, be based on additional conditionsor values beyond those recited. Headings, lists, and numbering includedherein are for ease of explanation only and are not meant to belimiting.

While the present subject matter has been described in detail withrespect to specific embodiments thereof, it will be appreciated thatthose skilled in the art, upon attaining an understanding of theforegoing, may readily produce alterations to, variations of, andequivalents to such embodiments. Accordingly, it should be understoodthat the present disclosure has been presented for purposes of examplerather than limitation, and does not preclude the inclusion of suchmodifications, variations, and/or additions to the present subjectmatter as would be readily apparent to one of ordinary skill in the art.

1. A method in which one or more processing devices perform operationscomprising: receiving a search query comprising a keyword; identifying,based on the search query, a video file comprising keyframes, thekeyframes associated with content tags; determining matching scores forthe keyframes, respectively, wherein a matching score for a keyframe isdetermined based on a number of content tags associated with thekeyframe that match the keyword; and generating, for display, a dynamicpreview of a plurality of keyframes associated with the highestrespective matching scores, wherein the dynamic preview comprises anarrangement of the plurality of keyframes.
 2. The method of claim 1,wherein each of the keyframes represents a respective scene in the videofile.
 3. The method of claim 1, the operations further comprising:extracting content attributes from each keyframe of the plurality ofkeyframes; and generating, for each keyframe, content tags based on therespective content attributes.
 4. The method of claim 1, wherein thedynamic preview comprises a video clip, the method further comprising:displaying a search result comprising the dynamic preview; detecting auser input comprising a location proximate to the dynamic preview; andplaying back the video clip in response to the user input.
 5. The methodof claim 4, wherein generating the dynamic preview comprises:identifying a time duration from a user profile; and combining theplurality of keyframes into the video clip having the time duration. 6.The method of claim 1, wherein the dynamic preview comprises a collage,a GIF, a playback loop.
 7. The method of claim 1, the operations furthercomprising: determining, for each keyframe, an aesthetic score; andwherein the arrangement of the plurality of keyframes is based in parton the aesthetic scores of each of the plurality of keyframes.
 8. Themethod of claim 7, wherein the aesthetic score comprises a parameterindicating one or more of a quality, a balancing, a harmony, asharpness, a lighting, or a symmetry.
 9. The method of claim 7, whereineach of the plurality of keyframes represents a scene in the video file,and wherein generating, for display, the dynamic preview comprises:computing a set of total aesthetic scores for each scene, wherein atotal aesthetic score for a scene comprises a sum of the aestheticscores for a subset of frames within the scene; and selecting theplurality of keyframes based in part on the plurality of keyframes beingincluded in respective scenes having larger total aesthetic scores thanother scenes of the video file.
 10. The method of claim 7, wherein eachof the plurality of keyframes respectively comprise a timestamp, andwherein generating, for display, the dynamic preview comprises:determining an order of the plurality of keyframes based on achronological order of the timestamps; and arranging the plurality ofkeyframes based at least in part on the chronological order and theaesthetic score associated with each of the plurality of keyframes. 11.A system comprising a processing device; and a non-transitorycomputer-readable medium communicatively coupled to the processingdevice and storing program code, wherein the processing device isconfigured for executing the program code and thereby performingoperations comprising: receiving a search query that includes a keyword;identifying, based on the search query, a video file comprisingkeyframes, the keyframes associated with content tags; determiningmatching scores for the keyframes, respectively, wherein each matchingscore for a respective keyframe indicates a respective number of contenttags associated with the respective keyframe that match the keyword; andgenerating, for display, a dynamic preview of a plurality of keyframesassociated with the highest respective matching scores, wherein thedynamic preview comprises an arrangement of the plurality of keyframes.12. The system of claim 11, wherein each of the plurality of keyframesrepresents a respective scene in the video file.
 13. The system of claim11, the operations further comprising: extracting content attributesfrom each keyframe of the plurality of keyframes; and generating, foreach keyframe, content tags based on the respective content attributes.14. The system of claim 11, wherein the dynamic preview comprises avideo clip, the operations further comprising: instructing a clientdevice to display a search result comprising the dynamic preview;detecting a user input comprising a location proximate to the dynamicpreview; and instructing the client device to play back the video clipin response to the user input.
 15. The system of claim 11, theoperations further comprising determining an aesthetic score for eachkeyframe of the plurality of keyframes, wherein the processing device isconfigured to determine the arrangement of the plurality of keyframesbased in part on the aesthetic scores of each of the plurality ofkeyframes.
 16. The system of claim 15, wherein the aesthetic scorecomprises a parameter indicating one or more of a quality, a balancing,a harmony, a sharpness, a lighting, or a symmetry.
 17. The system ofclaim 15, wherein each of the plurality of keyframes represents a scenein the video file, and wherein generating the dynamic preview comprises:computing a set of total aesthetic scores for each scene, wherein atotal aesthetic score for a scene comprises a sum of the aestheticscores for a subset of frames within the scene; and selecting theplurality of keyframes based in part on the plurality of keyframes beingincluded in respective scenes having larger total aesthetic scores thanother scenes of the video file.
 18. The system of claim 15, wherein eachof the plurality of keyframes respectively comprise a timestamp, andwherein generating, for display, the dynamic preview comprises:determining an order of the plurality of keyframes based on achronological order of the timestamps; and arranging the plurality ofkeyframes based at least in part on the chronological order and theaesthetic score associated with each of the plurality of keyframes. 19.A non-transitory computer-readable medium having program code storedthereon, wherein the program code, when executed by one or moreprocessing devices, performs operations comprising: receiving a searchquery that includes a keyword; a step for generating a dynamic previewof video content matching the keyword; and outputting the dynamicpreview.
 20. The non-transitory computer-readable medium of claim 19,wherein the dynamic preview comprises: (i) a thumbnail collage or (ii) apreview video including a set of keyframes selected from the videocontent.